1. Technical Field
The present application generally relates to a method and device for diagnosing and/or predicting the presence, progression and/or treatment effect of a disease. In particular, the present application relates to a method and device for diagnosing and/or predicting the presence, progression and/or treatment effect of a disease characterized by retinal pathological changes in a subject.
2. Description of the Related Art
Color retina image is the only way with direct inspection of blood vessel and its pathology change through the whole body. It not only reflects retina disease but also reflects risks of systemic diseases, such as stroke.
Large epidemiological studies showed many retina characteristics which related to long term hypertension and/or diabetes were associated with stroke incidence or prevalence. Those retina characteristics detected included retina vessel diameter, arteriole-venule nipping, retinopathy, etc. However, there are new retina characteristics that could provide more information on stroke patient classification, such as vessel tortuosity, vessel asymmetry. Furthermore, the interaction of the retina characteristics also provided very important information to classify patients with stroke from those without stroke. We have shown in our clinical study that the result contributed to the classification of patients with stoke from those without stroke using retina vessel tortuosity and asymmetry and interactions of retina characteristics.
Apart from the new retina information we detected manually for stroke classification, computerized automatic analytical system based on fractal analysis, high order spectral analysis, and statistical texture analysis can also classify patients with stroke from those without stoke based on the analysis of color retina images. The data extracted from the automatic system correlated well with the clinical retina characteristics and their interaction. With such correlations, we demonstrated that the automatic analytical system captured the clinically important characteristics over and above the level that can be done manually, this can be expanded to other eye diseases with possible clinical interpretation.
The retinal pathological changes have been shown to be associated with many diseases, including systematic diseases, e.g. stroke, hypertension, diabetes, cardiovascular diseases including coronary heart disease and cerebral vascular disease; and eye diseases, e.g. glaucoma, retinopathy due to prematurity, papilloedema, macular hole, and age-related macular degeneration.
Many important eye diseases as well as systemic diseases manifest themselves in the retina. Cardiovascular disease manifests itself in the retina in a number of ways. Hypertension and atherosclerosis cause changes in the ratio between the diameter of retinal arteries and veins, known as the A/V ratio. A decrease in A/V ratio such as thinning of the arteries and widening of veins, is associated with an increased risk of stroke [b1]. Recent research has also shown that the branching pattern of retinal arterial and venous systems have fractal characteristics [b2]. For instance, Patton N, Aslam T and et al., suggested that retinal vascular image analysis is a potential screening tool for cerebrovascular disease. They mentioned that the fractals offer a natural, global, comprehensive description of the retinal vascular tree because they take into account both the changes in retinal vessel caliber and changes in branching pattern. Other studies related with retinal vessels' characteristics are also provided by Mainster M. A., and Daxer A., they pointed out that the retinal arterial and venous patterns have fractal dimensions of 1.62+0.05 and 1.7+0.07, also the fractal dimension of retinal vessel patterns with neovascularisation at or near the optic disc (NVD) is about 1.8 comparing with the control group of about 1.7 [b3-4]. However, MacGillivary T. J., Doubal F. N. and et., compared monofractal and multifractal analysis of human retinal vasculature and they indicated that multifractal approach is more efficient for detecting small changes to the retinal vasculature. Therefore, it is reasonable to believe that monofractal and multifractal analysis of human retinal vasculature are both necessary. Hence, fractal geometry provides a global and more accurate description of anatomy of the eye than classical geometry. Fractal patterns characterize how vascular patterns span the retina and can therefore provide information about the relationship between vascular pattern and retinal disease.
Recently Hsu W., Lee M. L. and Wong T. Y. have developed a platform (patented) for automated analysis of a retinal image, including automatically tracing one or more paths of one or more vessels of a retinal image, and such obtained information may be useful in forming a diagnosis of a medical condition [b5]. It also developed an automated retinal image analysis system and/or used the fractal analysis technique to provide disease risk prediction, such as hypertension. However, the approach of tracing vessels is quite retinal image quality dependent comparing to other approaches. Also, the zone defining vessel measurements lack flexibility in practical applications, i.e., the image has to have the optic disc in the middle of the image, it may not cover all useful information if the optic disc is not placed in the middle. Moreover, some useful (or partially useful) non-retinal vasculature related information may have been missed and more importantly almost all of retinal image analysis ignored the effect from the interactions between factors of the vessel measurements, and/or with other factors such as High order spectra (HOS) and texture analysis related risk factors. Acharya R., Chua C. K. and et al., have found the application of non-linear features of the HOS was more suitable for the detection of shapes and thus they apply this technique for the identification of diabetes Retinopathy stages [b6]. Dobrescu R., Dobrescu M. and et al., applied the method based on combined texture and fractal analysis automatic to detect the malignancy of skin lesions [b7]. However, until now there is no comprehensive technique/approach using retinal images to provide disease risk prediction based on their complexity of characteristics (i.e., interaction between shapes, intensity, directionality and etc.).
We developed an automated disease detection system using retinal images. We first generate all possible risk factors from color images that may associated with diabetes retinopathy, stroke and/or other diseases. This includes some characteristics from the aspect of intensity changes, such as high order spectra, entropy and etc., and also from Gray Level Co-occurrence Matrix (or Haralick) and run-length matrix Texture Features. For instance, our previous study for retinal vessel patterns with neovascularisation detection has shown that there are number of significant of interactions among some high order spectra features and features related to the shape of vessels. Secondly, we stored all generated factors and applied penalized supervised logistic regression to reduce the dimension (or use random forest approach to extract important features). This procedure is used to generate potentially significant features associated with stroke and other diseases. Next we applied multi-model inference with Generalized Linear Models (MIGLM) to select the best model that generated all possible factors with their pairwise interactions. Finally we applied the Random Forest to assess its stroke classification performance. The advantage of using penalized supervised logistic regression and MIGLM are their interactive effects preserving properties. Random Forest approach is considerable a suitable method for non-linear classification in high-dimensional space [b8].